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Your search keyword '"Noh, Youngtae"' showing total 25 results
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25 results on '"Noh, Youngtae"'

1. Toward Data-Driven Digital Therapeutics Analytics: Literature Review and Research Directions

Author

Lee, Uichin, Jung, Gyuwon, Ma, Eun-Yeol, Kim, Jin San, Kim, Heepyung, Alikhanov, Jumabek, Noh, Youngtae, and Kim, Heeyoung

Subjects
Computer Science - Human-Computer Interaction
Abstract

With the advent of Digital Therapeutics (DTx), the development of software as a medical device (SaMD) for mobile and wearable devices has gained significant attention in recent years. Existing DTx evaluations, such as randomized clinical trials, mostly focus on verifying the effectiveness of DTx products. To acquire a deeper understanding of DTx engagement and behavioral adherence, beyond efficacy, a large amount of contextual and interaction data from mobile and wearable devices during field deployment would be required for analysis. In this work, the overall flow of the data-driven DTx analytics is reviewed to help researchers and practitioners to explore DTx datasets, to investigate contextual patterns associated with DTx usage, and to establish the (causal) relationship of DTx engagement and behavioral adherence. This review of the key components of data-driven analytics provides novel research directions in the analysis of mobile sensor and interaction datasets, which helps to iteratively improve the receptivity of existing DTx., Comment: This paper has been accepted by the IEEE/CAA Journal of Automatica Sinica

Published
2022

5. Toward Data-Driven Digital Therapeutics Analytics: Literature Review and Research Directions

Author

Lee, Uichin, Jung, Gyuwon, Ma, Eun-Yeol, Kim, Jin San, Kim, Heepyung, Alikhanov, Jumabek, Noh, Youngtae, and Kim, Heeyoung

Subjects
FOS: Computer and information sciences, Control and Optimization, Artificial Intelligence, Control and Systems Engineering, Computer Science - Human-Computer Interaction, Human-Computer Interaction (cs.HC), Information Systems
Abstract

With the advent of Digital Therapeutics (DTx), the development of software as a medical device (SaMD) for mobile and wearable devices has gained significant attention in recent years. Existing DTx evaluations, such as randomized clinical trials, mostly focus on verifying the effectiveness of DTx products. To acquire a deeper understanding of DTx engagement and behavioral adherence, beyond efficacy, a large amount of contextual and interaction data from mobile and wearable devices during field deployment would be required for analysis. In this work, the overall flow of the data-driven DTx analytics is reviewed to help researchers and practitioners to explore DTx datasets, to investigate contextual patterns associated with DTx usage, and to establish the (causal) relationship of DTx engagement and behavioral adherence. This review of the key components of data-driven analytics provides novel research directions in the analysis of mobile sensor and interaction datasets, which helps to iteratively improve the receptivity of existing DTx., This paper has been accepted by the IEEE/CAA Journal of Automatica Sinica

Published
2023

14. Evolutionary Tracking Algorithm Based on Combined Received Signal Strength and Angle of Arrival Measurements in Wireless Sensor Networks

Author

Caceres Najarro, Lismer Andres, Song, Iickho, Tomic, Slavisa, Salman, Muhammad, Noh, Youngtae, and Kim, Kiseon

Abstract

This article addresses the target tracking problem based on the received signal strength (RSS) and angle of arrival (AOA) in wireless sensor networks (WSNs). The tracking problem is formulated in the framework of the maximum a posteriori (MAP), in which the prior knowledge of moving target nodes (TNs) is exploited. Due to the fact that the cost function of the tracking problem is highly nonlinear and nonconvex, most of the existing algorithms tend to approximate and relax the cost function. As a result, the tracking accuracy is usually compromised. In this article, we propose a tracking algorithm based on evolutionary techniques that do not require an approximation of the cost function, resulting in a considerable improvement in tracking accuracy. The proposed tracking algorithm is compared with state-of-the-art algorithms such as the MAP, particle filter (PF), and Kalman filter (KF). Simulation and real experiment results demonstrate that the proposed tracking algorithm provides an improvement roughly by 16%, 11%, and 18% over the MAP, PF, and KF, respectively, in the tracking accuracy.

Published
2023
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17. CSI:DeSpy

Author

Salman, Muhammad, primary, Dao, Nguyen, additional, Lee, Uichin, additional, and Noh, Youngtae, additional

Published
2022
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